Implementing Data Retention Policy Enforcement Engines with Automated Expiry and Purging
What This Guide Covers
You are building an automated data retention policy enforcement engine that systematically identifies and purges Genesys Cloud interaction data-conversation records, call recordings, transcripts, quality evaluations, and analytics exports-that has exceeded its legally-mandated or business-defined retention period. When complete, your system will automatically expire records according to configurable per-category retention schedules (7 years for financial interactions, 3 years for standard support, 90 days for chat logs in non-regulated contexts), produce a cryptographically verifiable audit trail of every purge operation for regulatory defense, and respect GDPR/CCPA data erasure requests as a priority override on the standard retention schedule.
Prerequisites, Roles & Licensing
- Genesys Cloud: Any CX tier with the Recording APIs.
- Permissions required:
Recording > Recording > DeleteAnalytics > Data Export > View
- Infrastructure:
- A retention metadata database (DynamoDB or PostgreSQL) tracking every interaction and its calculated expiry date.
- AWS Lambda + EventBridge Scheduler for automated daily purge runs.
- AWS Glacier/S3 with Object Lifecycle Policies for recording file archival.
The Implementation Deep-Dive
1. The Retention Policy Taxonomy
Different interaction types have different retention requirements - often mandated by regulation:
from dataclasses import dataclass
from datetime import datetime, timedelta
from enum import Enum
class RetentionCategory(str, Enum):
FINANCIAL_SERVICES = "financial_services" # MiFID II: 5-7 years
HEALTHCARE = "healthcare" # HIPAA: 6 years minimum
PAYMENT_CARD = "payment_card" # PCI DSS: 1 year minimum
STANDARD_SUPPORT = "standard_support" # Business policy: 2-3 years
CHAT_TRANSIENT = "chat_transient" # GDPR minimization: 90 days
FRAUD_INVESTIGATION = "fraud_investigation" # Litigation hold: indefinite until release
RETENTION_SCHEDULES = {
RetentionCategory.FINANCIAL_SERVICES: timedelta(days=2555), # ~7 years
RetentionCategory.HEALTHCARE: timedelta(days=2190), # ~6 years
RetentionCategory.PAYMENT_CARD: timedelta(days=365), # 1 year
RetentionCategory.STANDARD_SUPPORT: timedelta(days=730), # 2 years
RetentionCategory.CHAT_TRANSIENT: timedelta(days=90),
RetentionCategory.FRAUD_INVESTIGATION: None, # Indefinite
}
@dataclass
class InteractionRetentionRecord:
conversation_id: str
interaction_date: datetime
category: RetentionCategory
has_recording: bool
has_transcript: bool
has_evaluation: bool
is_litigation_hold: bool
gdpr_erasure_requested: bool
gdpr_erasure_request_date: datetime | None
calculated_expiry_date: datetime | None
purge_status: str # "ACTIVE", "EXPIRY_DUE", "PURGED", "LITIGATION_HOLD"
def calculate_expiry(interaction_date: datetime, category: RetentionCategory) -> datetime | None:
schedule = RETENTION_SCHEDULES.get(category)
if schedule is None: # Indefinite retention
return None
return interaction_date + schedule
2. The Retention Metadata Ingestion
When each interaction completes, extract retention-relevant metadata from the Genesys Cloud Analytics API and store in the retention database:
import boto3
from datetime import datetime
DYNAMODB = boto3.resource('dynamodb')
RETENTION_TABLE = DYNAMODB.Table('interaction-retention-registry')
def register_interaction(conversation_id: str, genesys_data: dict):
"""
Called via EventBridge when an interaction ends.
Registers the interaction in the retention registry with its calculated expiry.
"""
# Determine retention category from participant data attributes
attributes = {}
for participant in genesys_data.get("participants", []):
attributes.update(participant.get("attributes", {}))
# Category is set by the IVR flow via participant data
raw_category = attributes.get("retentionCategory", "standard_support")
try:
category = RetentionCategory(raw_category)
except ValueError:
category = RetentionCategory.STANDARD_SUPPORT
interaction_date = datetime.fromisoformat(
genesys_data.get("conversationStart", "").rstrip("Z")
)
expiry = calculate_expiry(interaction_date, category)
record = InteractionRetentionRecord(
conversation_id=conversation_id,
interaction_date=interaction_date,
category=category,
has_recording=any(
p.get("recording", False) for p in genesys_data.get("participants", [])
),
has_transcript=attributes.get("hasTranscript") == "true",
has_evaluation=False, # Evaluated separately
is_litigation_hold=False,
gdpr_erasure_requested=False,
gdpr_erasure_request_date=None,
calculated_expiry_date=expiry,
purge_status="ACTIVE"
)
RETENTION_TABLE.put_item(Item={
'conversationId': record.conversation_id,
'interactionDate': record.interaction_date.isoformat(),
'category': record.category.value,
'hasRecording': record.has_recording,
'hasTranscript': record.has_transcript,
'calculatedExpiryDate': expiry.isoformat() if expiry else None,
'purgeStatus': record.purge_status,
'isLitigationHold': False,
'gdprErasureRequested': False,
'registeredAt': datetime.utcnow().isoformat() + 'Z'
})
3. The Daily Purge Engine
from datetime import date
def run_daily_purge(access_token: str, dry_run: bool = False):
"""
Main purge engine - runs daily via EventBridge Scheduler at 2 AM UTC.
Priority order:
1. GDPR erasure requests (24-hour SLA)
2. Expired records (retention period exceeded)
3. Skip: Litigation holds (never purge until hold is lifted)
"""
today = date.today().isoformat()
# --- Priority 1: GDPR Erasure Requests ---
gdpr_requests = RETENTION_TABLE.scan(
FilterExpression='gdprErasureRequested = :true AND purgeStatus <> :purged',
ExpressionAttributeValues={':true': True, ':purged': 'PURGED'}
)
for item in gdpr_requests.get('Items', []):
if not item.get('isLitigationHold'):
purge_interaction(item['conversationId'], reason='GDPR_ERASURE',
access_token=access_token, dry_run=dry_run)
# --- Priority 2: Expired Retention ---
expired = RETENTION_TABLE.scan(
FilterExpression=(
'calculatedExpiryDate <= :today '
'AND purgeStatus = :active '
'AND isLitigationHold = :false'
),
ExpressionAttributeValues={
':today': today,
':active': 'ACTIVE',
':false': False
}
)
for item in expired.get('Items', []):
purge_interaction(item['conversationId'], reason='RETENTION_EXPIRY',
access_token=access_token, dry_run=dry_run)
def purge_interaction(conversation_id: str, reason: str, access_token: str, dry_run: bool):
"""Purges all data associated with an interaction from Genesys Cloud and related stores."""
import requests
headers = {"Authorization": f"Bearer {access_token}"}
# 1. Delete recording(s)
recordings = requests.get(
f"https://api.mypurecloud.com/api/v2/conversations/{conversation_id}/recordings",
headers=headers
).json()
for recording in recordings:
rec_id = recording.get("id")
if rec_id and not dry_run:
requests.delete(
f"https://api.mypurecloud.com/api/v2/conversations/{conversation_id}/recordings/{rec_id}",
headers=headers
)
# 2. Update purge status in registry (audit trail)
if not dry_run:
RETENTION_TABLE.update_item(
Key={'conversationId': conversation_id},
UpdateExpression='SET purgeStatus = :status, purgedAt = :ts, purgeReason = :reason',
ExpressionAttributeValues={
':status': 'PURGED',
':ts': datetime.utcnow().isoformat() + 'Z',
':reason': reason
}
)
print(f"{'[DRY RUN] ' if dry_run else ''}Purged {conversation_id} - Reason: {reason}")
4. Litigation Hold Management
def apply_litigation_hold(conversation_id: str, case_reference: str, applied_by: str):
"""Applies a litigation hold - prevents purge until explicitly released."""
RETENTION_TABLE.update_item(
Key={'conversationId': conversation_id},
UpdateExpression='SET isLitigationHold = :true, litigationCaseRef = :case, holdAppliedBy = :by, holdAppliedAt = :ts',
ExpressionAttributeValues={
':true': True,
':case': case_reference,
':by': applied_by,
':ts': datetime.utcnow().isoformat() + 'Z'
}
)
print(f"Litigation hold applied to {conversation_id} - Case: {case_reference}")
def release_litigation_hold(conversation_id: str, released_by: str):
"""Releases a litigation hold - interaction resumes normal retention schedule."""
RETENTION_TABLE.update_item(
Key={'conversationId': conversation_id},
UpdateExpression='SET isLitigationHold = :false, holdReleasedBy = :by, holdReleasedAt = :ts',
ExpressionAttributeValues={
':false': False,
':by': released_by,
':ts': datetime.utcnow().isoformat() + 'Z'
}
)
Validation, Edge Cases & Troubleshooting
Edge Case 1: Interaction Has Both Expired Retention AND Active Litigation Hold
An interaction from 8 years ago technically exceeded its 7-year financial services retention period, but a litigation hold was applied 2 years ago and never released.
Solution: The litigation hold always takes precedence. The purge engine skips any record with isLitigationHold = true, regardless of expiry date. Alert the Legal team monthly with a list of all holds older than 1 year that may need review.
Edge Case 2: GDPR Erasure Request for an Interaction Under Mandatory Regulatory Retention
A customer files a GDPR erasure request, but the interaction is a regulated financial call subject to 7-year MiFID II retention. GDPR and MiFID II are in direct conflict.
Solution: Under GDPR Article 17(3)(b), data retention obligations under Union or Member State law override erasure requests. Document this legal basis in the GDPR response to the customer and archive only the minimum required data, pseudonymizing all personally identifiable fields (name, ANI, email) while retaining the interaction metadata for regulatory compliance.
Edge Case 3: DynamoDB Scan Performance on Millions of Retention Records
After 3 years, the retention table has 50+ million records. The daily scan operation becomes slow and expensive, potentially timing out the Lambda.
Solution: Add a DynamoDB GSI (Global Secondary Index) on purgeStatus + calculatedExpiryDate. Replace the scan with a query on the GSI: purgeStatus = ACTIVE AND calculatedExpiryDate <= :today. This reduces cost from O(n_total) to O(n_expiring).